Pregnancy and fetal outcomes following natalizumab exposure in pregnancy. A prospective, controlled observational study
Bibliographic record
Abstract
BACKGROUND: Safety data on first-trimester natalizumab exposure are scarce, as natalizumab is usually withdrawn three months before pregnancy. OBJECTIVE: The objective of this paper is to investigate the fetal safety of exposure to natalizumab (Tysabri(®)) during the first trimester of pregnancy using disease-matched (DM) and healthy control (HC) comparison groups. METHODS: A total of 101 German women with RRMS exposed to natalizumab during the first trimester of pregnancy were identified. Birth outcomes in the exposed group were compared to a DM group (N = 78) with or without exposure to other disease-modifying drugs, and an HC group (N = 97). RESULTS: A total of 77, 69 and 92 live births occurred in the Exposed, DM and HC groups, respectively. The rates of major malformations (p = 0.67), low birth weight (<2500 grams) (p = 1.0) and premature birth (p = 0.37) did not differ among groups. Higher miscarriage rates (p = 0.002) and lower birth weights (p = 0.001) occurred among the Exposed and DM groups, as compared to the HC; however, there was no significant difference between the Exposed and DM groups. CONCLUSION: Exposure to natalizumab in early pregnancy does not appear to increase the risk of adverse pregnancy outcomes in comparison to a DM group not exposed to natalizumab.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".